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The Capacity Diagram Method procedure, which is based on R-�-T (Reduction-ductility-Period) relationships, is investigated to compare with the modified CDM procedure.
The CDM procedure, which is based on R-µ-T(Reduction - ductility - Period) relationships, is investigated.
(5) where ω is the weight of the building, Ry is a reduction factor due to ductility (Ry = Ae/Ay), and Ay is the pseudo acceleration, which is related to corresponding ductility, the natural period, and the damping ratio of the elastic system.
The detail data was derived from the results of separate interior and exterior beam-column sub-assemblage tests by Reinhorn [5].
These tests provided the data on yield strength and hysteretic behavior.

The results show that reductions in errors of predicted maximum impact reaction force (compared to experimental data) were achieved from 29% using Static ADM to 14% using SRD ADM and from 35% using Static ADM to 15% using SRD ADM respectively for impactor speeds of 2 ms-1 and 5 ms-1.
In the present study, extrapolated test data obtained at low strain-rate tests are input to a three-point impact simulation using ABAQUS/VUSDFLD and compared with published three-point impact test data.
Material properties data obtained from tensile tests [1] and extrapolated up to 5 s-1, were represented as Eq. 1, with the data shown as in Table 1.
Four components of energy release rate, associated with longitudinal failure in tension and compression (Gftc and Gfcc, respectively) and transverse failure in tension and compression (Gmtc and Gmcc, respectively) were extracted from published experimental data [6].
The prediction of maximum reaction force for cross-ply using SRD ADM at impactor speed 2 ms-1 is better than using static ADM with an error of 14% and 29% respectively when compared with impact tests data and the prediction of maximum reaction force for cross-ply using SRD ADM at impactor speed of 5 ms-1 is better than static ADM, with an error of 15% and 35% respectively when compared with impact test data.

The chip reduction coefficient (CRC) and von Mises stress (VMS) were the output responses.
Subsequently, the chip reduction coefficients (ratio of formed and un-deformed chip thicknesses) were determined.
Data for the mean response Level Speed, (N) Feed, (f) DOC, (d) 1 -5.1277 -8.6458 -6.5573 2 -5.1142 -5.8871 -4.5501 3 -6.2072 -1.9162 -5.3417 Delta, (max-min) 1.093 6.7296 2.0072 Rank 3 1 2 It is seen that the optimal solution is attained at N2 f3 d2 i.e., at (0, 1, 0) (Table 7).
Validation of data obtained by Taguchi-FIS simulation.
Therefore, it is evident that the data obtained by the Taguchi-FIS simulation is validated.

Now, developed countries have mastered the higher carbon abatement technologies but with the small space of emission reduction and high per unit reduction costs, while developing countries is in a period of rapid development of economy with huge energy consumption, more serious environmental pollution and lack of efficient about saving technology in energy, but with the big space of emission reduction and low cost for per unit of emission reduction.
The following content structure is as follow: in the second part, according to the Reviva Hasson et al. (2010) one-shot public-goods game, we will deduce that CDM is the Game equilibrium result, in the third part by using data analysis and case study we analyze two dilemmas exist in CDM which lead the uncertainty of its development, in the fourth part, conclusion.
CH4 reduction has 40 projects, but only 2 of them use foreign technology.
These data suggest that the effect of the technology transfer of CDM is not obvious.
Carbon emission reduction technology is the most cutting-edge which is excludable and rivalrous, while low-cost promotion and application of these carbon emission reduction technology is clearly the most effective way to mitigate climate change.

The equilibrium data obtained from adsorption studies was found to fit both Langmuir and Freundlich adsorption isotherms.
Reduction in silica content could be due to leaching of silica from external epidermis of rice husk.
The equilibrium data were also analyzed for the adsorption isotherm studies.
The Langmuir and Freundlich isotherms were used in order to interpret equilibrium isotherm data.
Fig. 4 indicates that both Langmuir and Freundlich isotherms give good fit for the equilibrium data of Pb(II) ion adsorption on RHAC.

Based on the comparison of computing results and experimental data, we analyzes the differences between ventilated cavitating flow fields with and without considered the compressibility of gases.
The results show that the simulation data of cavity form and resistance, which takes the compressibility of gases into account, accord well with the experimental ones.
Introduction The drag reduction technique with supercavity, which can reduce the resistance of the body in the water by 90%, has a wide application prospect[1-2].
At present, most studies on ventilation supercavitating flow mainly focus on the drag-reduction mechanism of ventilated supercavity at normal temperature, prediction of cavity form, the effect of parameters, such as cavitation number, ventilation flow rate, cavitator parameters, Froud number and so on, on the cavitating flow[3-6].
Due to the density in cavity around ventilation hole decreases and the time-averaged velocity is relative higher, leading to the drag reduction to some extent, the drag coefficient decreases as the ventilation temperature increasing.

The dependence of volume reduction (V/Vo) and lattice parameters (a and c) on pressure were obtained successfully.
The calculated energy gaps showed a reduction in the band gap width of 4H and 6H with the pressure increase while 2H band gap increased gradually with pressure.
The reduction in the bond lengths became more symmetric at pressures higher than 90GPa, and with almost no changes in the unit cell angles.
Therefore theoretical calculations are comparable to the experimental data.
The variations in the electronic structure of the polytypes showed a reduction in the band gap width of 4H and 6H with the pressure increase while 2H band gap increased gradually with pressure.

It showed that the percentage of make span reduction due to lot streaming in permutation flow shop is 54% when compared to consistent sublots with intermingling case.
They found that, under the same sublot type, although increasing number of sublots will reduce makespan, the marginal reduction in makespan will decrease with the increase of the number of sublots.
Other input data is summarized in Table 2.Two situations were considered: 1) none of machines needs maintenance, and 2) all machines need preventive maintenance.
Input data on different machines Machine 1 Machine 2 Machine 3 Machine 4 Duration of maintenance tasks 20 30 40 40 Early completion time of maintenance tasks 250 410 400 520 Late completion time of maintenance tasks 280 420 420 540 Results of the Problem LINGOsolver defined the model of example as amixed integer linearproblem (MILP) and used the branch and bound (B-and-B) method to solve it.
The percentage of makespan reduction due to lot streaming in permutation flowshop is 54% (compared to the best makespan).

It can get the core of attributes by reducing redundant samples from the current data set, and then use the core as decision rules to classify the observed or measured imperfect data.
Unfortunately, the Rough Set Theory can only deal with such data sets that only contains discrete attributes while the reality is that most data sets contains both discrete and consecutive attributes at the same time.
The essence of discretization is to treat some consecutive data points as indistinguishable, and use an uniform symbol to instead all of them.
References [1] Zhao Jun, Data discretization methods based on rough set theory, Journal of Chinese Computer System, 25(1) (2004) 60-64
[8] Liu Kai, Wang Yi-Nao, Pang Yan-Jun, Data discretization methods based on clustering neural network, Computer Science, 28(5) (2001) 136-137,168

Thus the emphasis has shifted to improving gross margins through cost reduction.
It is very important to use actual time measured rather than the average of previous data.
The various gap areas observed from the current state map (Fig. 1) are: 1) Work in process between various operations is considerably high. 2) Station cycle time variation is more, which lead to imbalance in the production. 3) Production lead time is quite high. 4) Production is below the installed capacity as per the data. 5) Improper utilization of the workers.
The results of value stream mapping (Table 1) compares before current state and after future state data for crank case.
The future state value mapping of the crank case shows reduction in total lead time by 50.5 %.